Example HDF5 Insights¶
This Jupyter Notebook should give a brief overview how to programmatically analyze the HDF5 files produced by mycelyso. Please note that you can always inspect these files with mycelyso Inspector as well, this tutorial should just give you a hint how to open these files if you might want to write your own analyses.
First, it is assumed that an output.h5 is present in the current
directory, with an analysis of the example dataset contained.
You can fetch the example dataset by running get-dataseth.sh or
download it manually at https://zenodo.org/record/376281.
Afterwards, analyze it with:
> python -m mycelyso S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff -t BoxDetection=1
Afterwards, you will have an output.h5 in the residing in the
directory.
We will be using Pandas to read our data, while the non-tabular data could easily be read with any other HDF5 compatible tool, the tabular data is layed out in a chunked format particular to Pandas, and as such it is easiest to open it with Pandas.
First, some general setup …
%matplotlib inline
%config InlineBackend.figure_formats=['svg']
import pandas
pandas.options.display.max_columns = None
import numpy as np
import networkx as nx
from networkx.readwrite import GraphMLReader
from matplotlib import pyplot, ticker
pyplot.rcParams.update({
'figure.figsize': (10, 6), 'svg.fonttype': 'none',
'font.sans-serif': 'Arial', 'font.family': 'sans-serif',
'image.cmap': 'gray_r', 'image.interpolation': 'none'
})
Opening the HDF5 file¶
We will load the output.h5 using pandas.HDFStore …
store = pandas.HDFStore('output.h5', 'r')
store
<class 'pandas.io.pytables.HDFStore'>
File path: output.h5
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/result_table frame (shape->[1,208])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/result_table_collected frame (shape->[136,27])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000001 frame (shape->[22,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000002 frame (shape->[29,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000003 frame (shape->[11,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000004 frame (shape->[23,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000005 frame (shape->[16,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000006 frame (shape->[14,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000007 frame (shape->[12,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000008 frame (shape->[9,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000009 frame (shape->[17,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000010 frame (shape->[11,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000011 frame (shape->[8,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000012 frame (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000013 frame (shape->[10,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000014 frame (shape->[5,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000015 frame (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000016 frame (shape->[5,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000017 frame (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000018 frame (shape->[8,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000019 frame (shape->[8,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_individual_track_table_aux_tables/track_table_aux_tables_000000020 frame (shape->[7,8])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/_mapping_track_table_aux_tables/track_table_aux_tables_000000000 frame (shape->[20,2])
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/tables/track_table/track_table_000000000 frame (shape->[20,66])
Now let’s dive a bit into the HDF5 file.
Remember that HDF5 stands for Hierarchical Data Format 5 …
root = store.get_node('/')
print("Root:")
print(repr(root))
print()
print("/results:")
print(repr(root.results))
Root:
/ (RootGroup) ''
children := ['results' (Group)]
/results:
/results (Group) ''
children := ['mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff' (Group)]
The key names are dependent on the on-disk path of the analyzed file. Assuming there is only one file analyzed with one position in the file, we pick the first …
for image_file in root.results:
print(image_file)
for position in image_file:
print(position)
break
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected (Group) ''
We can now investigate what data is available for that particular position
There is e.g., (binary) data, there are images, and there are various tabular datasets
print("data")
print(position.data)
for node in position.data:
print(node)
print()
print("nodes")
print(position.images)
for node in position.images:
print(node)
print()
data
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/banner (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/graphml (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/overall_graphml (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/tunables (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/data/version (Group) ''
nodes
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/images (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/images/binary (Group) ''
/results/mycelyso_S_lividans_TK24_Complex_Medium_nd046_138_ome_tiff/pos_000000000_t_Collected/images/skeleton (Group) ''
Accessing Graph and Image Data¶
Let’s for example start with pulling out an image from the file, and displaying it …
binary_images = list(position.images.binary)
skeleton_images = list(position.images.skeleton)
n = 120
total = len(binary_images)
assert 0 <= n < total
print("Total count of images: %d" % (total,))
fig, (ax_l, ax_r) = pyplot.subplots(1, 2, sharey=True)
fig.suptitle('Images of Timepoint #%d:' % (n,))
ax_l.imshow(binary_images[n])
ax_l.set_title('Binary Image')
ax_r.imshow(skeleton_images[n])
ax_r.set_title('Skeleton')
Total count of images: 136
Text(0.5,1,'Skeleton')
Let’s now take a look at the graph data present for the position, display it and overlay it onto the image data …
# The graph structure is saved in GraphML
draw_parameters = dict(node_size=25, node_color='darkgray', linewidths=0, edge_color='darkgray', with_labels=False)
#graphml_data = list([np.array(graphml).tobytes() for graphml in list(position.data.graphml)])
graphml_data = list(position.data.graphml)
graph, = GraphMLReader()(string=np.array(graphml_data[n]).tobytes())
# the following draw function needs separate positions...
# each node has its position saved as attributes:
example_node_id = list(sorted(graph.node.keys()))[1]
print("Example node: %s: %r" % (example_node_id, graph.node[example_node_id],))
other_node_id = list(sorted(graph.adj[example_node_id].keys(), reverse=True))[0]
print("Some other node: %s" % (other_node_id,))
print("The distance between the two nodes is: %.2f px" % (graph.adj[example_node_id][other_node_id]['weight']))
pyplot.title('Graph Representation of Timepoint #%d:' % (n,))
# first draw the graph,
pos = {n_id: (n['x'], n['y']) for n_id, n in graph.node.items()}
nx.draw_networkx(graph, pos=pos, **draw_parameters)
example_nodes = [graph.node[node_id] for node_id in [example_node_id, other_node_id]]
# mark on top the two choosen sample nodes
pyplot.scatter([p['x'] for p in example_nodes], [p['y'] for p in example_nodes], zorder=2)
# then show the corresponding binarized image
pyplot.imshow(binary_images[n])
Example node: 1: {'x': 543.0, 'y': 91.0}
Some other node: 4
The distance between the two nodes is: 192.05 px
<matplotlib.image.AxesImage at 0x7f89d9770128>
Accessing Tabular Data¶
In the next few cells we’ll take a look at the tabular data stored in the HDF5 file.
There is for example the result_table, which contains compounded
information about the whole position:
result_table = store[position.result_table._v_pathname]
result_table
| _mapping_track_table_aux_tables | banner | covered_area_linear_regression_intercept | covered_area_linear_regression_pvalue | covered_area_linear_regression_rvalue | covered_area_linear_regression_slope | covered_area_linear_regression_stderr | covered_area_logarithmic_regression_intercept | covered_area_logarithmic_regression_pvalue | covered_area_logarithmic_regression_rvalue | covered_area_logarithmic_regression_slope | covered_area_logarithmic_regression_stderr | covered_area_optimized_linear_regression_begin | covered_area_optimized_linear_regression_begin_index | covered_area_optimized_linear_regression_end | covered_area_optimized_linear_regression_end_index | covered_area_optimized_linear_regression_intercept | covered_area_optimized_linear_regression_pvalue | covered_area_optimized_linear_regression_rvalue | covered_area_optimized_linear_regression_slope | covered_area_optimized_linear_regression_stderr | covered_area_optimized_logarithmic_regression_begin | covered_area_optimized_logarithmic_regression_begin_index | covered_area_optimized_logarithmic_regression_end | covered_area_optimized_logarithmic_regression_end_index | covered_area_optimized_logarithmic_regression_intercept | covered_area_optimized_logarithmic_regression_pvalue | covered_area_optimized_logarithmic_regression_rvalue | covered_area_optimized_logarithmic_regression_slope | covered_area_optimized_logarithmic_regression_stderr | covered_ratio_linear_regression_intercept | covered_ratio_linear_regression_pvalue | covered_ratio_linear_regression_rvalue | covered_ratio_linear_regression_slope | covered_ratio_linear_regression_stderr | covered_ratio_logarithmic_regression_intercept | covered_ratio_logarithmic_regression_pvalue | covered_ratio_logarithmic_regression_rvalue | covered_ratio_logarithmic_regression_slope | covered_ratio_logarithmic_regression_stderr | covered_ratio_optimized_linear_regression_begin | covered_ratio_optimized_linear_regression_begin_index | covered_ratio_optimized_linear_regression_end | covered_ratio_optimized_linear_regression_end_index | covered_ratio_optimized_linear_regression_intercept | covered_ratio_optimized_linear_regression_pvalue | covered_ratio_optimized_linear_regression_rvalue | covered_ratio_optimized_linear_regression_slope | covered_ratio_optimized_linear_regression_stderr | covered_ratio_optimized_logarithmic_regression_begin | covered_ratio_optimized_logarithmic_regression_begin_index | covered_ratio_optimized_logarithmic_regression_end | covered_ratio_optimized_logarithmic_regression_end_index | covered_ratio_optimized_logarithmic_regression_intercept | covered_ratio_optimized_logarithmic_regression_pvalue | covered_ratio_optimized_logarithmic_regression_rvalue | covered_ratio_optimized_logarithmic_regression_slope | covered_ratio_optimized_logarithmic_regression_stderr | filename | filename_complete | graph_edge_count_linear_regression_intercept | graph_edge_count_linear_regression_pvalue | graph_edge_count_linear_regression_rvalue | graph_edge_count_linear_regression_slope | graph_edge_count_linear_regression_stderr | graph_edge_count_logarithmic_regression_intercept | graph_edge_count_logarithmic_regression_pvalue | graph_edge_count_logarithmic_regression_rvalue | graph_edge_count_logarithmic_regression_slope | graph_edge_count_logarithmic_regression_stderr | graph_edge_count_optimized_linear_regression_begin | graph_edge_count_optimized_linear_regression_begin_index | graph_edge_count_optimized_linear_regression_end | graph_edge_count_optimized_linear_regression_end_index | graph_edge_count_optimized_linear_regression_intercept | graph_edge_count_optimized_linear_regression_pvalue | graph_edge_count_optimized_linear_regression_rvalue | graph_edge_count_optimized_linear_regression_slope | graph_edge_count_optimized_linear_regression_stderr | graph_edge_count_optimized_logarithmic_regression_begin | graph_edge_count_optimized_logarithmic_regression_begin_index | graph_edge_count_optimized_logarithmic_regression_end | graph_edge_count_optimized_logarithmic_regression_end_index | graph_edge_count_optimized_logarithmic_regression_intercept | graph_edge_count_optimized_logarithmic_regression_pvalue | graph_edge_count_optimized_logarithmic_regression_rvalue | graph_edge_count_optimized_logarithmic_regression_slope | graph_edge_count_optimized_logarithmic_regression_stderr | graph_edge_length_linear_regression_intercept | graph_edge_length_linear_regression_pvalue | graph_edge_length_linear_regression_rvalue | graph_edge_length_linear_regression_slope | graph_edge_length_linear_regression_stderr | graph_edge_length_logarithmic_regression_intercept | graph_edge_length_logarithmic_regression_pvalue | graph_edge_length_logarithmic_regression_rvalue | graph_edge_length_logarithmic_regression_slope | graph_edge_length_logarithmic_regression_stderr | graph_edge_length_optimized_linear_regression_begin | graph_edge_length_optimized_linear_regression_begin_index | graph_edge_length_optimized_linear_regression_end | graph_edge_length_optimized_linear_regression_end_index | graph_edge_length_optimized_linear_regression_intercept | graph_edge_length_optimized_linear_regression_pvalue | graph_edge_length_optimized_linear_regression_rvalue | graph_edge_length_optimized_linear_regression_slope | graph_edge_length_optimized_linear_regression_stderr | graph_edge_length_optimized_logarithmic_regression_begin | graph_edge_length_optimized_logarithmic_regression_begin_index | graph_edge_length_optimized_logarithmic_regression_end | graph_edge_length_optimized_logarithmic_regression_end_index | graph_edge_length_optimized_logarithmic_regression_intercept | graph_edge_length_optimized_logarithmic_regression_pvalue | graph_edge_length_optimized_logarithmic_regression_rvalue | graph_edge_length_optimized_logarithmic_regression_slope | graph_edge_length_optimized_logarithmic_regression_stderr | graph_endpoint_count_linear_regression_intercept | graph_endpoint_count_linear_regression_pvalue | graph_endpoint_count_linear_regression_rvalue | graph_endpoint_count_linear_regression_slope | graph_endpoint_count_linear_regression_stderr | graph_endpoint_count_logarithmic_regression_intercept | graph_endpoint_count_logarithmic_regression_pvalue | graph_endpoint_count_logarithmic_regression_rvalue | graph_endpoint_count_logarithmic_regression_slope | graph_endpoint_count_logarithmic_regression_stderr | graph_endpoint_count_optimized_linear_regression_begin | graph_endpoint_count_optimized_linear_regression_begin_index | graph_endpoint_count_optimized_linear_regression_end | graph_endpoint_count_optimized_linear_regression_end_index | graph_endpoint_count_optimized_linear_regression_intercept | graph_endpoint_count_optimized_linear_regression_pvalue | graph_endpoint_count_optimized_linear_regression_rvalue | graph_endpoint_count_optimized_linear_regression_slope | graph_endpoint_count_optimized_linear_regression_stderr | graph_endpoint_count_optimized_logarithmic_regression_begin | graph_endpoint_count_optimized_logarithmic_regression_begin_index | graph_endpoint_count_optimized_logarithmic_regression_end | graph_endpoint_count_optimized_logarithmic_regression_end_index | graph_endpoint_count_optimized_logarithmic_regression_intercept | graph_endpoint_count_optimized_logarithmic_regression_pvalue | graph_endpoint_count_optimized_logarithmic_regression_rvalue | graph_endpoint_count_optimized_logarithmic_regression_slope | graph_endpoint_count_optimized_logarithmic_regression_stderr | graph_junction_count_linear_regression_intercept | graph_junction_count_linear_regression_pvalue | graph_junction_count_linear_regression_rvalue | graph_junction_count_linear_regression_slope | graph_junction_count_linear_regression_stderr | graph_junction_count_logarithmic_regression_intercept | graph_junction_count_logarithmic_regression_pvalue | graph_junction_count_logarithmic_regression_rvalue | graph_junction_count_logarithmic_regression_slope | graph_junction_count_logarithmic_regression_stderr | graph_junction_count_optimized_linear_regression_begin | graph_junction_count_optimized_linear_regression_begin_index | graph_junction_count_optimized_linear_regression_end | graph_junction_count_optimized_linear_regression_end_index | graph_junction_count_optimized_linear_regression_intercept | graph_junction_count_optimized_linear_regression_pvalue | graph_junction_count_optimized_linear_regression_rvalue | graph_junction_count_optimized_linear_regression_slope | graph_junction_count_optimized_linear_regression_stderr | graph_junction_count_optimized_logarithmic_regression_begin | graph_junction_count_optimized_logarithmic_regression_begin_index | graph_junction_count_optimized_logarithmic_regression_end | graph_junction_count_optimized_logarithmic_regression_end_index | graph_junction_count_optimized_logarithmic_regression_intercept | graph_junction_count_optimized_logarithmic_regression_pvalue | graph_junction_count_optimized_logarithmic_regression_rvalue | graph_junction_count_optimized_logarithmic_regression_slope | graph_junction_count_optimized_logarithmic_regression_stderr | graph_node_count_linear_regression_intercept | graph_node_count_linear_regression_pvalue | graph_node_count_linear_regression_rvalue | graph_node_count_linear_regression_slope | graph_node_count_linear_regression_stderr | graph_node_count_logarithmic_regression_intercept | graph_node_count_logarithmic_regression_pvalue | graph_node_count_logarithmic_regression_rvalue | graph_node_count_logarithmic_regression_slope | graph_node_count_logarithmic_regression_stderr | graph_node_count_optimized_linear_regression_begin | graph_node_count_optimized_linear_regression_begin_index | graph_node_count_optimized_linear_regression_end | graph_node_count_optimized_linear_regression_end_index | graph_node_count_optimized_linear_regression_intercept | graph_node_count_optimized_linear_regression_pvalue | graph_node_count_optimized_linear_regression_rvalue | graph_node_count_optimized_linear_regression_slope | graph_node_count_optimized_linear_regression_stderr | graph_node_count_optimized_logarithmic_regression_begin | graph_node_count_optimized_logarithmic_regression_begin_index | graph_node_count_optimized_logarithmic_regression_end | graph_node_count_optimized_logarithmic_regression_end_index | graph_node_count_optimized_logarithmic_regression_intercept | graph_node_count_optimized_logarithmic_regression_pvalue | graph_node_count_optimized_logarithmic_regression_rvalue | graph_node_count_optimized_logarithmic_regression_slope | graph_node_count_optimized_logarithmic_regression_stderr | meta_pos | meta_t | metadata | overall_graphml | track_table | track_table_aux_tables | tunables | version | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | -209.368383 | 2.532537e-24 | 0.734525 | 0.008969 | 0.000716 | NaN | NaN | NaN | NaN | NaN | 39345.176144 | 65 | 78338.287784 | 130 | -994.607791 | 1.677850e-22 | 0.884206 | 0.020906 | 0.001391 | 47147.290182 | 78 | 78338.287784 | 130 | -2.303539 | 1.205272e-63 | 0.998338 | 0.000119 | 9.727372e-07 | -0.028316 | 2.532537e-24 | 0.734525 | 0.000001 | 9.681107e-08 | NaN | NaN | NaN | NaN | NaN | 39345.176144 | 65 | 78338.287784 | 130 | -0.134516 | 1.677850e-22 | 0.884206 | 0.000003 | 1.881839e-07 | 47147.290182 | 78 | 78338.287784 | 130 | -11.211959 | 1.205272e-63 | 0.998338 | 0.000119 | 9.727372e-07 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | /mycelyso/S_lividans_TK24_Complex_Medium_nd046... | -28.385481 | 6.207684e-15 | 0.604935 | 0.001209 | 0.000138 | NaN | NaN | NaN | NaN | NaN | 54942.33151 | 91 | 81340.338617 | 136 | -445.363712 | 4.994880e-15 | 0.873456 | 0.007417 | 0.000631 | 54942.33151 | 91 | 81340.338617 | 136 | -8.772886 | 3.728079e-27 | 0.966964 | 0.000178 | 0.000007 | -189.301864 | 6.799061e-22 | 0.706908 | 0.008101 | 0.0007 | NaN | NaN | NaN | NaN | NaN | 39345.176144 | 65 | 81340.338617 | 136 | -1139.396801 | 1.110753e-23 | 0.877234 | 0.023302 | 0.001535 | 47147.290182 | 78 | 81340.338617 | 136 | -2.78033 | 3.708275e-66 | 0.997503 | 0.000123 | 0.000001 | -10.07769 | 1.265490e-16 | 0.633514 | 0.000465 | 0.000049 | NaN | NaN | NaN | NaN | NaN | 54942.33151 | 91 | 81340.338617 | 136 | -157.23131 | 1.693324e-16 | 0.892893 | 0.002662 | 0.000205 | 54942.33151 | 91 | 81340.338617 | 136 | -6.582629 | 2.789480e-35 | 0.986286 | 0.000136 | 0.000003 | -11.862853 | 3.182848e-15 | 0.61005 | 0.00048 | 0.000054 | NaN | NaN | NaN | NaN | NaN | 54942.33151 | 91 | 78338.287784 | 130 | -110.650737 | 1.217144e-17 | 0.929788 | 0.001887 | 0.000123 | 62741.237858 | 104 | 78338.287784 | 130 | -6.592383 | 2.291108e-19 | 0.983605 | 0.000134 | 0.000005 | -21.940543 | 5.114994e-16 | 0.623593 | 0.000945 | 0.000102 | NaN | NaN | NaN | NaN | NaN | 54942.33151 | 91 | 81340.338617 | 136 | -333.239213 | 8.587192e-16 | 0.883997 | 0.005585 | 0.00045 | 54942.33151 | 91 | 81340.338617 | 136 | -7.695156 | 7.400355e-30 | 0.975361 | 0.00016 | 0.000006 | 0 | -1 | 0 | 0 | 21 | 0 | 0 |
Then there is the result_table_collected, which contains collected
information about every single frame of the time series of one position:
result_table_collected = store[position.result_table_collected._v_pathname]
result_table_collected
| area | binary | calibration | covered_area | covered_ratio | crop_b | crop_l | crop_r | crop_t | filename | graph_edge_count | graph_edge_length | graph_endpoint_count | graph_junction_count | graph_node_count | graphml | image_sha256_hash | input_height | input_width | meta_pos | meta_t | metadata | shift_x | shift_y | skeleton | timepoint | tunables_hash | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7393.965475 | 0 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 0 | FLHyF8lkwKef9Q9yEWsgOFzYc4qFCpKyirTRsfsR7/g= | 128.245 | 57.655 | 0 | 0 | 3.0 | 3.0 | 0 | 356.745246 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 1 | 7393.965475 | 1 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 1 | 494VC0oqeVoCO/0IYeZnowKoultCZe+iYTW5/xRIfXQ= | 128.245 | 57.655 | 0 | 1 | 0.0 | 0.0 | 1 | 954.331815 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 2 | 7393.965475 | 2 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 2 | kg3NjTylgz8a9Z7wnSSmEgxZHxP0tAaj1dxCWuGaMec= | 128.245 | 57.655 | 0 | 2 | -3.0 | -2.0 | 2 | 1548.970068 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 3 | 7393.965475 | 3 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 3 | S6KmMEQmUxMdLbpBnAyTs01xKaGIBjtgP1g/Raq9zqg= | 128.245 | 57.655 | 0 | 3 | -6.0 | -4.0 | 3 | 2152.429459 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 4 | 7393.965475 | 4 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 4 | EM4yxCU5tahPntThJVNQtAus2R69jCszYck1ZHFDhX4= | 128.245 | 57.655 | 0 | 4 | -4.0 | -5.0 | 4 | 2754.315663 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 5 | 7393.965475 | 5 | 0.065 | 11.766625 | 0.001591 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 5.5 | 22.899434 | 5 | 0 | 5 | 5 | c+9vT5uE1ozpUvzrkp1EQcG03GORVwOTjxjrZqRPQn4= | 128.245 | 57.655 | 0 | 5 | -9.0 | -5.0 | 5 | 3349.845006 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 6 | 7393.965475 | 6 | 0.065 | 21.931975 | 0.002966 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 15.5 | 41.708488 | 11 | 1 | 12 | 6 | xvSVz5s+PLa4Sj8oHuz83v2KXW8W//20bogdtZYFYps= | 128.245 | 57.655 | 0 | 6 | -8.0 | -4.0 | 6 | 3954.256373 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 7 | 7393.965475 | 7 | 0.065 | 18.877300 | 0.002553 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 11.5 | 38.285793 | 9 | 0 | 9 | 7 | LDTibVqcoMtulQHwHHQUgtHV1xUFeIk+AnZxudajBL0= | 128.245 | 57.655 | 0 | 7 | -7.0 | -6.0 | 7 | 4548.847011 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 8 | 7393.965475 | 8 | 0.065 | 11.306100 | 0.001529 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 9.0 | 21.241934 | 7 | 0 | 7 | 8 | a3O6yoCLPmRkTBo/O7VFHi62Yc2lxx3w7b4BXKCskPk= | 128.245 | 57.655 | 0 | 8 | -8.0 | -5.0 | 8 | 5149.800172 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 9 | 7393.965475 | 9 | 0.065 | 19.612450 | 0.002652 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 19.0 | 37.788097 | 12 | 3 | 15 | 9 | R8zOCET5fdw+UveaB1/weWXLjxRewlTgsh6JAe1cl2A= | 128.245 | 57.655 | 0 | 9 | -9.0 | -3.0 | 9 | 5747.743609 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 10 | 7393.965475 | 10 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 10 | bwg71JuWU476X8llCcc7HIpK2W+telAz9PmUgbbG3GI= | 128.245 | 57.655 | 0 | 10 | -5.0 | -4.0 | 10 | 6346.900296 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 11 | 7393.965475 | 11 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 11 | 58LrEPmBMhek4StJU2otfhjiYm3Im5//cRvAgkj05mo= | 128.245 | 57.655 | 0 | 11 | -4.0 | -6.0 | 11 | 6946.751259 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 12 | 7393.965475 | 12 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 12 | gpa2zMzRM8K2KE6Lr2AxIaLb+F/gdhuX8XrpRDvxlv8= | 128.245 | 57.655 | 0 | 12 | -4.0 | -5.0 | 12 | 7543.367799 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 13 | 7393.965475 | 13 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 13 | /KsfU2o48XgIY2W1oXsqn6nHxUHs/J/Wv1Z7nj0ZZOk= | 128.245 | 57.655 | 0 | 13 | -7.0 | -4.0 | 13 | 8144.258055 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 14 | 7393.965475 | 14 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 14 | DxApSHRIomGrqNpBitjQEo7QhFrEynEJ8ZmKJrvplnY= | 128.245 | 57.655 | 0 | 14 | -2.0 | -4.0 | 14 | 8747.270315 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 15 | 7393.965475 | 15 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 15 | Co1f04WWFLOobP5pOvdHqNsqTWIINGAZDb73YRPrEMo= | 128.245 | 57.655 | 0 | 15 | -2.0 | -5.0 | 15 | 9342.921723 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 16 | 7393.965475 | 16 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 16 | c4qXuABN6T/+Kqhl1Mu+dDc4DeaFoA6/+/P0O1oXurs= | 128.245 | 57.655 | 0 | 16 | -4.0 | -5.0 | 16 | 9944.746882 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 17 | 7393.965475 | 17 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 17 | rW1XbA7JoDeobq+O88KRJPV2sIinal/XU9yWVK5duzs= | 128.245 | 57.655 | 0 | 17 | -4.0 | -6.0 | 17 | 10546.833173 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 18 | 7393.965475 | 18 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 18 | 4VRvPwGvoi38OdaAH11CJhGkpwIjLmbVoXU9VPxOjpw= | 128.245 | 57.655 | 0 | 18 | -2.0 | -6.0 | 18 | 11142.278725 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 19 | 7393.965475 | 19 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 19 | lGBlKy1m69uZFS4+z2qOu01U4TAepF98z5Qy0rgpKq4= | 128.245 | 57.655 | 0 | 19 | -4.0 | -5.0 | 19 | 11748.821861 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 20 | 7393.965475 | 20 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 20 | suQeImrAqjZDCOeXIo7jXiAo1EbKWi7RHyjg/K92eeo= | 128.245 | 57.655 | 0 | 20 | -5.0 | -5.0 | 20 | 12354.980074 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 21 | 7393.965475 | 21 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 21 | g/nSp2+luy9+GumMUPJZjNTIq/fEsVAZDftXGWzWeT8= | 128.245 | 57.655 | 0 | 21 | -3.0 | -5.0 | 21 | 12944.765587 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 22 | 7393.965475 | 22 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 22 | BovPeepsLCC72gmUDKXJRPCAlQ62ZbcCw6khY2exoVQ= | 128.245 | 57.655 | 0 | 22 | -2.0 | -7.0 | 22 | 13545.854889 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 23 | 7393.965475 | 23 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 23 | 6ddbC20/XQcL62LLIthfgKK1+hZ471gas/x47xAErgU= | 128.245 | 57.655 | 0 | 23 | -5.0 | -6.0 | 23 | 14146.223223 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 24 | 7393.965475 | 24 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 24 | sKWUFcK2/AkvT7VsD479I5RyUSh42fg419mJ+7NGElc= | 128.245 | 57.655 | 0 | 24 | -2.0 | -4.0 | 24 | 14748.335994 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 25 | 7393.965475 | 25 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 25 | 5j1pPeyhTmt8DTk2PXJJY+qXzQLof67lF3iSqHQ7fYs= | 128.245 | 57.655 | 0 | 25 | 3.0 | -6.0 | 25 | 15343.735260 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 26 | 7393.965475 | 26 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 26 | uhGgSzijhmEGPdb+vseY5QkDZXRZDiSaAgKqGYgLNY4= | 128.245 | 57.655 | 0 | 26 | 1.0 | -7.0 | 26 | 15953.863397 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 27 | 7393.965475 | 27 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 27 | VXsOEGRfM7I4HccxdR/32rUj3tZrSypiQk5SFztQ8BQ= | 128.245 | 57.655 | 0 | 27 | 0.0 | -4.0 | 27 | 16542.758080 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 28 | 7393.965475 | 28 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 28 | vjbM5PQTup+sY2oxC7pA0TkBf5sE8TQnR+EkW02XyPU= | 128.245 | 57.655 | 0 | 28 | 0.0 | -4.0 | 28 | 17142.263416 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 29 | 7393.965475 | 29 | 0.065 | 0.000000 | 0.000000 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 0.0 | 0.000000 | 0 | 0 | 0 | 29 | SO9ouW//cxEuF6b5JioGV6TFtg5CsMLKAoTdx8TPIis= | 128.245 | 57.655 | 0 | 29 | 0.0 | -7.0 | 29 | 17740.279887 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 106 | 7393.965475 | 106 | 0.065 | 210.666950 | 0.028492 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 19.5 | 170.239411 | 9 | 8 | 17 | 106 | uAHtyApJzNnPOYnpdVOIKWkvOYSmlCkO8ZC9u2gta5o= | 128.245 | 57.655 | 0 | 106 | 0.0 | -1.0 | 106 | 63947.249755 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 107 | 7393.965475 | 107 | 0.065 | 207.519325 | 0.028066 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 20.0 | 180.808115 | 10 | 7 | 17 | 107 | 25jns/xT4PLo4Jxf505fLowf+A2qcVQmWq4ke+5VCMI= | 128.245 | 57.655 | 0 | 107 | 4.0 | -2.0 | 107 | 64543.707035 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 108 | 7393.965475 | 108 | 0.065 | 219.763375 | 0.029722 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 21.5 | 190.435276 | 11 | 7 | 18 | 108 | OWqQprg2kii5dkmOoNCNbmM2z3lehAazAPO9IRYf9Xo= | 128.245 | 57.655 | 0 | 108 | 2.0 | -1.0 | 108 | 65139.869557 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 109 | 7393.965475 | 109 | 0.065 | 247.859625 | 0.033522 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 25.5 | 195.382602 | 12 | 10 | 22 | 109 | 1qt2o2cQ3+57QE0ZsjDBJPnuBVSWuafV54gucUCPje8= | 128.245 | 57.655 | 0 | 109 | -1.0 | 0.0 | 109 | 65741.778848 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 110 | 7393.965475 | 110 | 0.065 | 264.658225 | 0.035794 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 25.5 | 210.104377 | 13 | 10 | 23 | 110 | fq3wG1zJ0pYaf1oRLGPElzHf1YE1Qx/TNhCJecgfw48= | 128.245 | 57.655 | 0 | 110 | 0.0 | -1.0 | 110 | 66340.189219 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 111 | 7393.965475 | 111 | 0.065 | 280.556900 | 0.037944 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 39.5 | 235.773869 | 18 | 15 | 33 | 111 | vSbq+a0wytKuNcRRbUhf8pTJSyWM4kGIuD4SO1R5lh8= | 128.245 | 57.655 | 0 | 111 | -1.0 | -1.0 | 111 | 66943.783533 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 112 | 7393.965475 | 112 | 0.065 | 294.051550 | 0.039769 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 35.5 | 248.187748 | 16 | 14 | 30 | 112 | EyE6YpZWqRtaLGY6P7Ls5SbX4NOCZSIt+79qYEa7CfQ= | 128.245 | 57.655 | 0 | 112 | -2.0 | -1.0 | 112 | 67544.224723 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 113 | 7393.965475 | 113 | 0.065 | 316.444050 | 0.042798 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 36.5 | 260.646633 | 17 | 14 | 31 | 113 | xMiJu6s5Aibr9FDuX53pjMfDo/NdaTfDU1JBizujn+M= | 128.245 | 57.655 | 0 | 113 | -3.0 | -1.0 | 113 | 68144.223215 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 114 | 7393.965475 | 114 | 0.065 | 342.820725 | 0.046365 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 40.0 | 281.374211 | 19 | 15 | 34 | 114 | K+xpRjw5CaAnpr5Wn+S3JBznhdGApuFuWaRgIzjrD98= | 128.245 | 57.655 | 0 | 114 | -2.0 | -2.0 | 114 | 68741.153508 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 115 | 7393.965475 | 115 | 0.065 | 370.257875 | 0.050076 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 40.5 | 312.852562 | 18 | 16 | 34 | 115 | Mb4MgU9eSza1UKpwZMoYe9vFydo+CgkQIXXlqImQsT0= | 128.245 | 57.655 | 0 | 115 | -3.0 | -2.0 | 115 | 69343.336711 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 116 | 7393.965475 | 116 | 0.065 | 400.344100 | 0.054145 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 46.5 | 336.659457 | 22 | 17 | 39 | 116 | YhrWJOJHelwSMWZ5cireuPWOQerJ3ncgmYWSDmrdeq0= | 128.245 | 57.655 | 0 | 116 | -6.0 | 0.0 | 116 | 69940.686151 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 117 | 7393.965475 | 117 | 0.065 | 433.286425 | 0.058600 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 47.5 | 368.660910 | 20 | 18 | 38 | 117 | sE4DE63xAmb5NKVMReP7pab2izYSVM6UJAm5DkN0VXg= | 128.245 | 57.655 | 0 | 117 | -5.0 | -1.0 | 117 | 70540.386399 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 118 | 7393.965475 | 118 | 0.065 | 481.265525 | 0.065089 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 46.5 | 411.026463 | 20 | 18 | 38 | 118 | 81kFT/ZS0drUpl6kKYXzQw/XjlQzxIzPAmd3nL11+jg= | 128.245 | 57.655 | 0 | 118 | -4.0 | -3.0 | 118 | 71141.753863 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 119 | 7393.965475 | 119 | 0.065 | 528.095425 | 0.071422 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 46.5 | 442.766625 | 21 | 19 | 40 | 119 | M82K/jsBao445C6NKVnTNij+l6tWyNlSw353uGNiDLY= | 128.245 | 57.655 | 0 | 119 | -2.0 | -3.0 | 119 | 71748.778771 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 120 | 7393.965475 | 120 | 0.065 | 588.665025 | 0.079614 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 52.0 | 501.050286 | 24 | 19 | 43 | 120 | uzYdD+ar88aFulsNSqkm0WcNqly45OVPfXdiC2PoGn4= | 128.245 | 57.655 | 0 | 120 | 1.0 | -1.0 | 120 | 72342.288541 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 121 | 7393.965475 | 121 | 0.065 | 637.928525 | 0.086277 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 57.5 | 542.781477 | 25 | 22 | 47 | 121 | Xnoke73h5X3pqffz22tt/XS5zFL58NQRj3FRRLRVdh0= | 128.245 | 57.655 | 0 | 121 | 1.0 | -1.0 | 121 | 72942.162923 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 122 | 7393.965475 | 122 | 0.065 | 616.833100 | 0.083424 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 60.0 | 561.784642 | 25 | 23 | 48 | 122 | /xYS1ZdSkDs7iGCM7EUOEplVF6IvONSJfQS/gUjYfbo= | 128.245 | 57.655 | 0 | 122 | 3.0 | -3.0 | 122 | 73543.257127 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 123 | 7393.965475 | 123 | 0.065 | 735.441525 | 0.099465 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 73.5 | 630.276997 | 31 | 29 | 60 | 123 | u74v7IA9x4990zS2p78PeME6W+CjG3X2WQCoCt4zpzM= | 128.245 | 57.655 | 0 | 123 | -1.0 | -1.0 | 123 | 74140.149509 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 124 | 7393.965475 | 124 | 0.065 | 780.298350 | 0.105532 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 79.5 | 682.208179 | 32 | 31 | 63 | 124 | OoMxDjS6CVZqgFUIt9i3uE3edYm+cQgUGHmVAfoMCpk= | 128.245 | 57.655 | 0 | 124 | -2.0 | -2.0 | 124 | 74739.753889 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 125 | 7393.965475 | 125 | 0.065 | 821.783625 | 0.111142 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 90.5 | 720.402085 | 34 | 37 | 71 | 125 | QunbYZfVig1yXaR7CahU9lp7tbutNgRNCV2trlfH2ag= | 128.245 | 57.655 | 0 | 125 | -2.0 | -3.0 | 125 | 75342.294086 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 126 | 7393.965475 | 126 | 0.065 | 840.644025 | 0.113693 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 94.5 | 750.562416 | 37 | 37 | 74 | 126 | WxArF1YP7mcIyfJ5BwCADhyzu3HjH/EArvQ/ughWwag= | 128.245 | 57.655 | 0 | 126 | -2.0 | -1.0 | 126 | 75940.191470 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 127 | 7393.965475 | 127 | 0.065 | 853.923200 | 0.115489 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 109.5 | 773.762895 | 42 | 42 | 84 | 127 | JNQ60hSinRysv9iHUDlvWbajC3pxmetHJCy4umA78k8= | 128.245 | 57.655 | 0 | 127 | -4.0 | -1.0 | 127 | 76540.684802 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 128 | 7393.965475 | 128 | 0.065 | 908.451050 | 0.122864 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 120.5 | 826.868598 | 45 | 48 | 93 | 128 | DM/8MVnM0IlU4i6dsYVg6pvjKOEQ0G4+ie+lacKNzto= | 128.245 | 57.655 | 0 | 128 | -4.0 | -1.0 | 128 | 77143.279996 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 129 | 7393.965475 | 129 | 0.065 | 928.084625 | 0.125519 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 137.0 | 854.965852 | 56 | 51 | 107 | 129 | vJ6ddwOacUWSEuKgYavl9YYYDhHkZ22SGGd6i5nCv5s= | 128.245 | 57.655 | 0 | 129 | -3.0 | -3.0 | 129 | 77739.277364 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 130 | 7393.965475 | 130 | 0.065 | 995.219875 | 0.134599 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 164.5 | 931.113953 | 59 | 65 | 124 | 130 | EMfLUv7Hu2b7NJOcjA4BGr748D3i+uQYqBR3D1+Olyk= | 128.245 | 57.655 | 0 | 130 | -5.0 | -2.0 | 130 | 78338.287784 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 131 | 7393.965475 | 131 | 0.065 | 1042.451150 | 0.140987 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 177.5 | 982.963010 | 63 | 70 | 133 | 131 | PZNqH1IrTEzf49uuqlNYGyjdzrx4buvzZJonsP68Etg= | 128.245 | 57.655 | 0 | 131 | -5.0 | -2.0 | 131 | 78943.246053 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 132 | 7393.965475 | 132 | 0.065 | 1043.646825 | 0.141148 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 186.0 | 982.474030 | 61 | 75 | 136 | 132 | 1fAdMd5ruK5y/zwSWuqWqcCelW2sBWElCNhU6zhaovY= | 128.245 | 57.655 | 0 | 132 | 1.0 | -2.0 | 132 | 79540.788485 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 133 | 7393.965475 | 133 | 0.065 | 1023.569625 | 0.138433 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 191.5 | 1029.805448 | 73 | 73 | 146 | 133 | bUVUBoCP3NhJCFHhGjHu3czbHLuJQxTkg2iCE6jqeJs= | 128.245 | 57.655 | 0 | 133 | 7.0 | -3.0 | 133 | 80140.704110 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 134 | 7393.965475 | 134 | 0.065 | 1035.670025 | 0.140070 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 218.5 | 1074.944651 | 85 | 80 | 165 | 134 | mh5CCpK+8DkzZ0Jb95x+XF1OLShiK/B/12l78G/UVgY= | 128.245 | 57.655 | 0 | 134 | 5.0 | -1.0 | 134 | 80741.868186 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... | |
| 135 | 7393.965475 | 135 | 0.065 | 1135.342000 | 0.153550 | 1978 | 754 | 1642 | 4 | S_lividans_TK24_Complex_Medium_nd046_138.ome.tiff | 279.0 | 1201.021333 | 96 | 113 | 209 | 135 | ZdYdZ9ud5oLdOJP5XVD1633MzPXv4GR6EjzZLtCgpNo= | 128.245 | 57.655 | 0 | 135 | 4.0 | 0.0 | 135 | 81340.338617 | VERSION:1:SHA256:iNevP0W3i5SsPhjSobMn0xCxU+e/Y... |
136 rows × 27 columns
The per-frame informations contain e.g. the graph length (i.e. the mycelium length), which can be plotted over time:
timepoint = result_table_collected.timepoint / (60*60)
length = result_table_collected.graph_edge_length
pyplot.title('Length over Time')
pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')
pyplot.plot(timepoint, length)
[<matplotlib.lines.Line2D at 0x7f89d964edd8>]
Last but not least, we will look at mycelium level tracking data in the
track_table. The track_table is a level deeper in the HDF5
structure, next to tables with individual tracks.
track_table = store[list(position.tables.track_table)[0]._v_pathname]
track_table
| aux_table | count | duration | logarithmic_normalized_regression_intercept | logarithmic_normalized_regression_pvalue | logarithmic_normalized_regression_rvalue | logarithmic_normalized_regression_slope | logarithmic_normalized_regression_stderr | logarithmic_plain_regression_intercept | logarithmic_plain_regression_pvalue | logarithmic_plain_regression_rvalue | logarithmic_plain_regression_slope | logarithmic_plain_regression_stderr | maximum_distance | maximum_distance_num | minimum_distance | minimum_distance_num | normalized_regression_intercept | normalized_regression_pvalue | normalized_regression_rvalue | normalized_regression_slope | normalized_regression_stderr | optimized_logarithmic_normalized_regression_begin | optimized_logarithmic_normalized_regression_begin_index | optimized_logarithmic_normalized_regression_end | optimized_logarithmic_normalized_regression_end_index | optimized_logarithmic_normalized_regression_intercept | optimized_logarithmic_normalized_regression_pvalue | optimized_logarithmic_normalized_regression_rvalue | optimized_logarithmic_normalized_regression_slope | optimized_logarithmic_normalized_regression_stderr | optimized_logarithmic_regression_begin | optimized_logarithmic_regression_begin_index | optimized_logarithmic_regression_end | optimized_logarithmic_regression_end_index | optimized_logarithmic_regression_intercept | optimized_logarithmic_regression_pvalue | optimized_logarithmic_regression_rvalue | optimized_logarithmic_regression_slope | optimized_logarithmic_regression_stderr | optimized_normalized_regression_begin | optimized_normalized_regression_begin_index | optimized_normalized_regression_end | optimized_normalized_regression_end_index | optimized_normalized_regression_intercept | optimized_normalized_regression_pvalue | optimized_normalized_regression_rvalue | optimized_normalized_regression_slope | optimized_normalized_regression_stderr | optimized_regression_begin | optimized_regression_begin_index | optimized_regression_end | optimized_regression_end_index | optimized_regression_intercept | optimized_regression_pvalue | optimized_regression_rvalue | optimized_regression_slope | optimized_regression_stderr | plain_regression_intercept | plain_regression_pvalue | plain_regression_rvalue | plain_regression_slope | plain_regression_stderr | timepoint_begin | timepoint_center | timepoint_end | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 22 | 12596.588071 | -7.600037 | 1.711806e-24 | 0.997499 | 0.000182 | 0.000003 | -5.825405 | 1.711806e-24 | 0.997499 | 0.000182 | 0.000003 | 57.906361 | 1.0 | 5.898107 | 1.0 | -29.091215 | 1.307881e-14 | 0.975429 | 0.000686 | 0.000035 | 42345.743439 | 0 | 54942.331510 | 21 | -7.700575 | 1.019580e-23 | 0.997723 | 0.000184 | 0.000003 | 42345.743439 | 0 | 54942.331510 | 21 | -5.925944 | 1.019580e-23 | 0.997723 | 0.000184 | 0.000003 | 42345.743439 | 0 | 54942.331510 | 21 | -27.740900 | 4.146086e-14 | 0.976412 | 0.000657 | 0.000033 | 42345.743439 | 0 | 54942.331510 | 21 | -163.618808 | 4.146086e-14 | 0.976412 | 0.003874 | 0.000197 | -171.583111 | 1.307881e-14 | 0.975429 | 0.004046 | 0.000204 | 42345.743439 | 48644.037475 | 54942.331510 |
| 1 | 1 | 29 | 16795.074294 | -4.477290 | 1.479542e-29 | 0.995785 | 0.000078 | 0.000001 | -0.891770 | 1.479542e-29 | 0.995785 | 0.000078 | 0.000001 | 141.762974 | 10.0 | 36.072102 | 1.0 | -9.033740 | 1.851875e-28 | 0.994916 | 0.000169 | 0.000003 | 58547.219791 | 0 | 75342.294086 | 29 | -4.477290 | 1.479542e-29 | 0.995785 | 0.000078 | 0.000001 | 58547.219791 | 0 | 75342.294086 | 29 | -0.891770 | 1.479542e-29 | 0.995785 | 0.000078 | 0.000001 | 58547.219791 | 0 | 75342.294086 | 29 | -9.033740 | 1.851875e-28 | 0.994916 | 0.000169 | 0.000003 | 58547.219791 | 0 | 75342.294086 | 29 | -325.865976 | 1.851875e-28 | 0.994916 | 0.006103 | 0.000119 | -325.865976 | 1.851875e-28 | 0.994916 | 0.006103 | 0.000119 | 58547.219791 | 66944.756938 | 75342.294086 |
| 2 | 2 | 11 | 5999.376544 | -15.868380 | 6.611100e-08 | 0.982767 | 0.000263 | 0.000016 | -15.056252 | 6.611100e-08 | 0.982767 | 0.000263 | 0.000016 | 11.042346 | 3.0 | 2.252696 | 1.0 | -40.384477 | 4.331466e-12 | 0.997984 | 0.000677 | 0.000014 | 60944.406990 | 0 | 66943.783533 | 11 | -15.868380 | 6.611100e-08 | 0.982767 | 0.000263 | 0.000016 | 60944.406990 | 0 | 66943.783533 | 11 | -15.056252 | 6.611100e-08 | 0.982767 | 0.000263 | 0.000016 | 60944.406990 | 0 | 66943.783533 | 11 | -40.384477 | 4.331466e-12 | 0.997984 | 0.000677 | 0.000014 | 60944.406990 | 0 | 66943.783533 | 11 | -90.973931 | 4.331466e-12 | 0.997984 | 0.001525 | 0.000032 | -90.973931 | 4.331466e-12 | 0.997984 | 0.001525 | 0.000032 | 60944.406990 | 63944.095262 | 66943.783533 |
| 3 | 3 | 23 | 13195.742519 | -5.125910 | 7.206502e-28 | 0.998462 | 0.000085 | 0.000001 | -1.566050 | 7.206502e-28 | 0.998462 | 0.000085 | 0.000001 | 111.155492 | 8.0 | 35.158275 | 1.0 | -8.832037 | 1.632991e-21 | 0.993789 | 0.000160 | 0.000004 | 60944.406990 | 0 | 74140.149509 | 23 | -5.125910 | 7.206502e-28 | 0.998462 | 0.000085 | 0.000001 | 60944.406990 | 0 | 74140.149509 | 23 | -1.566050 | 7.206502e-28 | 0.998462 | 0.000085 | 0.000001 | 60944.406990 | 0 | 74140.149509 | 23 | -8.832037 | 1.632991e-21 | 0.993789 | 0.000160 | 0.000004 | 60944.406990 | 0 | 74140.149509 | 23 | -310.519177 | 1.632991e-21 | 0.993789 | 0.005611 | 0.000137 | -310.519177 | 1.632991e-21 | 0.993789 | 0.005611 | 0.000137 | 60944.406990 | 67542.278249 | 74140.149509 |
| 4 | 4 | 16 | 8999.505265 | -21.524270 | 1.726340e-09 | 0.964524 | 0.000350 | 0.000026 | -21.201628 | 1.726340e-09 | 0.964524 | 0.000350 | 0.000026 | 44.678233 | 2.0 | 1.380772 | 1.0 | -223.912438 | 1.341946e-12 | 0.987353 | 0.003504 | 0.000150 | 63342.783276 | 0 | 72342.288541 | 15 | -22.604725 | 5.227294e-09 | 0.965929 | 0.000367 | 0.000027 | 63342.783276 | 0 | 72342.288541 | 15 | -22.282082 | 5.227294e-09 | 0.965929 | 0.000367 | 0.000027 | 63342.783276 | 0 | 72342.288541 | 15 | -215.096004 | 7.149179e-12 | 0.987747 | 0.003370 | 0.000148 | 63342.783276 | 0 | 72342.288541 | 15 | -296.998464 | 7.149179e-12 | 0.987747 | 0.004654 | 0.000204 | -309.171945 | 1.341946e-12 | 0.987353 | 0.004838 | 0.000208 | 63342.783276 | 67842.535909 | 72342.288541 |
| 5 | 5 | 14 | 7801.478279 | -23.838927 | 1.409835e-09 | 0.978374 | 0.000370 | 0.000023 | -23.438081 | 1.409835e-09 | 0.978374 | 0.000370 | 0.000023 | 41.950146 | 6.0 | 1.493087 | 1.0 | -185.970032 | 6.770607e-07 | 0.938601 | 0.002806 | 0.000298 | 65741.778848 | 0 | 73543.257127 | 14 | -23.838927 | 1.409835e-09 | 0.978374 | 0.000370 | 0.000023 | 65741.778848 | 0 | 73543.257127 | 14 | -23.438081 | 1.409835e-09 | 0.978374 | 0.000370 | 0.000023 | 65741.778848 | 0 | 73543.257127 | 14 | -185.970032 | 6.770607e-07 | 0.938601 | 0.002806 | 0.000298 | 65741.778848 | 0 | 73543.257127 | 14 | -277.669359 | 6.770607e-07 | 0.938601 | 0.004190 | 0.000445 | -277.669359 | 6.770607e-07 | 0.938601 | 0.004190 | 0.000445 | 65741.778848 | 69642.517987 | 73543.257127 |
| 6 | 6 | 12 | 6600.509694 | -43.722131 | 3.190588e-05 | 0.914065 | 0.000690 | 0.000097 | -46.108926 | 3.190588e-05 | 0.914065 | 0.000690 | 0.000097 | 23.474375 | 1.0 | 0.091924 | 1.0 | -2616.983966 | 1.009138e-08 | 0.983252 | 0.039382 | 0.002308 | 65741.778848 | 0 | 72342.288541 | 12 | -43.722131 | 3.190588e-05 | 0.914065 | 0.000690 | 0.000097 | 65741.778848 | 0 | 72342.288541 | 12 | -46.108926 | 3.190588e-05 | 0.914065 | 0.000690 | 0.000097 | 65741.778848 | 0 | 72342.288541 | 12 | -2616.983966 | 1.009138e-08 | 0.983252 | 0.039382 | 0.002308 | 65741.778848 | 0 | 72342.288541 | 12 | -240.563324 | 1.009138e-08 | 0.983252 | 0.003620 | 0.000212 | -240.563324 | 1.009138e-08 | 0.983252 | 0.003620 | 0.000212 | 65741.778848 | 69042.033694 | 72342.288541 |
| 7 | 7 | 9 | 4801.564644 | -30.119407 | 1.333225e-05 | 0.970975 | 0.000459 | 0.000043 | -30.124026 | 1.333225e-05 | 0.970975 | 0.000459 | 0.000043 | 12.486205 | 2.0 | 0.995391 | 1.0 | -138.693554 | 1.941910e-04 | 0.937004 | 0.002094 | 0.000295 | 66340.189219 | 0 | 71141.753863 | 9 | -30.119407 | 1.333225e-05 | 0.970975 | 0.000459 | 0.000043 | 66340.189219 | 0 | 71141.753863 | 9 | -30.124026 | 1.333225e-05 | 0.970975 | 0.000459 | 0.000043 | 66340.189219 | 0 | 71141.753863 | 9 | -138.693554 | 1.941910e-04 | 0.937004 | 0.002094 | 0.000295 | 66340.189219 | 0 | 71141.753863 | 9 | -138.054322 | 1.941910e-04 | 0.937004 | 0.002084 | 0.000294 | -138.054322 | 1.941910e-04 | 0.937004 | 0.002084 | 0.000294 | 66340.189219 | 68740.971541 | 71141.753863 |
| 8 | 8 | 17 | 9596.901268 | -22.271064 | 1.733538e-09 | 0.957300 | 0.000343 | 0.000027 | -21.766331 | 1.733538e-09 | 0.957300 | 0.000343 | 0.000027 | 57.441002 | 1.0 | 1.656543 | 1.0 | -242.634692 | 9.760773e-15 | 0.991591 | 0.003592 | 0.000121 | 66943.783533 | 0 | 76540.684802 | 17 | -22.271064 | 1.733538e-09 | 0.957300 | 0.000343 | 0.000027 | 66943.783533 | 0 | 76540.684802 | 17 | -21.766331 | 1.733538e-09 | 0.957300 | 0.000343 | 0.000027 | 66943.783533 | 0 | 76540.684802 | 17 | -242.634692 | 9.760773e-15 | 0.991591 | 0.003592 | 0.000121 | 66943.783533 | 0 | 76540.684802 | 17 | -401.934870 | 9.760773e-15 | 0.991591 | 0.005950 | 0.000201 | -401.934870 | 9.760773e-15 | 0.991591 | 0.005950 | 0.000201 | 66943.783533 | 71742.234168 | 76540.684802 |
| 9 | 9 | 11 | 5998.379390 | -29.357547 | 1.979017e-06 | 0.963090 | 0.000446 | 0.000042 | -29.552716 | 1.979017e-06 | 0.963090 | 0.000446 | 0.000042 | 15.516987 | 1.0 | 0.822696 | 1.0 | -194.768001 | 4.833247e-09 | 0.990387 | 0.002907 | 0.000135 | 66943.783533 | 0 | 72942.162923 | 11 | -29.357547 | 1.979017e-06 | 0.963090 | 0.000446 | 0.000042 | 66943.783533 | 0 | 72942.162923 | 11 | -29.552716 | 1.979017e-06 | 0.963090 | 0.000446 | 0.000042 | 66943.783533 | 0 | 72942.162923 | 11 | -194.768001 | 4.833247e-09 | 0.990387 | 0.002907 | 0.000135 | 66943.783533 | 0 | 72942.162923 | 11 | -160.234763 | 4.833247e-09 | 0.990387 | 0.002392 | 0.000111 | -160.234763 | 4.833247e-09 | 0.990387 | 0.002392 | 0.000111 | 66943.783533 | 69942.973228 | 72942.162923 |
| 10 | 10 | 8 | 4198.065326 | -48.245996 | 6.569861e-06 | 0.986152 | 0.000710 | 0.000049 | -48.636193 | 6.569861e-06 | 0.986152 | 0.000710 | 0.000049 | 11.633879 | 1.0 | 0.676924 | 1.0 | -275.022799 | 1.801673e-05 | 0.980590 | 0.004022 | 0.000328 | 68144.223215 | 0 | 72342.288541 | 8 | -48.245996 | 6.569861e-06 | 0.986152 | 0.000710 | 0.000049 | 68144.223215 | 0 | 72342.288541 | 8 | -48.636193 | 6.569861e-06 | 0.986152 | 0.000710 | 0.000049 | 68144.223215 | 0 | 72342.288541 | 8 | -275.022799 | 1.801673e-05 | 0.980590 | 0.004022 | 0.000328 | 68144.223215 | 0 | 72342.288541 | 8 | -186.169501 | 1.801673e-05 | 0.980590 | 0.002722 | 0.000222 | -186.169501 | 1.801673e-05 | 0.980590 | 0.002722 | 0.000222 | 68144.223215 | 70243.255878 | 72342.288541 |
| 11 | 11 | 7 | 3600.022869 | -47.066691 | 5.996060e-04 | 0.960051 | 0.000645 | 0.000084 | -46.870044 | 5.996060e-04 | 0.960051 | 0.000645 | 0.000084 | 14.274661 | 1.0 | 1.217315 | 1.0 | -220.575522 | 1.034271e-07 | 0.998762 | 0.003009 | 0.000067 | 73543.257127 | 0 | 77143.279996 | 6 | -53.306342 | 1.709971e-03 | 0.966044 | 0.000729 | 0.000097 | 73543.257127 | 0 | 77143.279996 | 6 | -53.109695 | 1.709971e-03 | 0.966044 | 0.000729 | 0.000097 | 73543.257127 | 0 | 77143.279996 | 6 | -215.315060 | 2.582288e-06 | 0.998688 | 0.002938 | 0.000075 | 73543.257127 | 0 | 77143.279996 | 6 | -262.106238 | 2.582288e-06 | 0.998688 | 0.003576 | 0.000092 | -268.509877 | 1.034271e-07 | 0.998762 | 0.003662 | 0.000082 | 73543.257127 | 75343.268562 | 77143.279996 |
| 12 | 12 | 10 | 5400.638976 | -53.862847 | 4.870276e-09 | 0.994214 | 0.000728 | 0.000028 | -54.398991 | 4.870276e-09 | 0.994214 | 0.000728 | 0.000028 | 24.758931 | 2.0 | 0.585000 | 1.0 | -604.374842 | 1.170390e-04 | 0.926455 | 0.008061 | 0.001158 | 74140.149509 | 0 | 79540.788485 | 9 | -56.645897 | 4.971286e-09 | 0.996980 | 0.000764 | 0.000023 | 74140.149509 | 0 | 79540.788485 | 9 | -57.182041 | 4.971286e-09 | 0.996980 | 0.000764 | 0.000023 | 74140.149509 | 0 | 79540.788485 | 9 | -546.454943 | 9.685289e-04 | 0.899213 | 0.007296 | 0.001342 | 74140.149509 | 0 | 79540.788485 | 9 | -319.676142 | 9.685289e-04 | 0.899213 | 0.004268 | 0.000785 | -353.559282 | 1.170390e-04 | 0.926455 | 0.004716 | 0.000677 | 74140.149509 | 76840.468997 | 79540.788485 |
| 13 | 13 | 5 | 2400.535293 | -16.201586 | 1.183782e-04 | 0.997865 | 0.000219 | 0.000008 | -12.961744 | 1.183782e-04 | 0.997865 | 0.000219 | 0.000008 | 43.186267 | 2.0 | 25.529693 | 1.0 | -20.386272 | 1.192592e-06 | 0.999900 | 0.000288 | 0.000002 | 74140.149509 | 0 | 76540.684802 | 5 | -16.201586 | 1.183782e-04 | 0.997865 | 0.000219 | 0.000008 | 74140.149509 | 0 | 76540.684802 | 5 | -12.961744 | 1.183782e-04 | 0.997865 | 0.000219 | 0.000008 | 74140.149509 | 0 | 76540.684802 | 5 | -20.386272 | 1.192592e-06 | 0.999900 | 0.000288 | 0.000002 | 74140.149509 | 0 | 76540.684802 | 5 | -520.455259 | 1.192592e-06 | 0.999900 | 0.007363 | 0.000060 | -520.455259 | 1.192592e-06 | 0.999900 | 0.007363 | 0.000060 | 74140.149509 | 75340.417155 | 76540.684802 |
| 14 | 14 | 7 | 3598.533895 | -16.153025 | 4.230040e-06 | 0.994534 | 0.000217 | 0.000010 | -12.834299 | 4.230040e-06 | 0.994534 | 0.000217 | 0.000010 | 60.150146 | 6.0 | 27.625136 | 1.0 | -23.722413 | 8.119266e-10 | 0.999822 | 0.000331 | 0.000003 | 74739.753889 | 0 | 78338.287784 | 6 | -17.117733 | 2.071218e-05 | 0.996282 | 0.000229 | 0.000010 | 74739.753889 | 0 | 78338.287784 | 6 | -13.799007 | 2.071218e-05 | 0.996282 | 0.000229 | 0.000010 | 74739.753889 | 0 | 78338.287784 | 6 | -23.752887 | 1.191299e-07 | 0.999718 | 0.000331 | 0.000004 | 74739.753889 | 0 | 78338.287784 | 6 | -656.176742 | 1.191299e-07 | 0.999718 | 0.009145 | 0.000109 | -655.334883 | 8.119266e-10 | 0.999822 | 0.009134 | 0.000077 | 74739.753889 | 76539.020837 | 78338.287784 |
| 15 | 15 | 5 | 2398.096314 | -68.488528 | 2.257037e-03 | 0.984743 | 0.000904 | 0.000092 | -68.048039 | 2.257037e-03 | 0.984743 | 0.000904 | 0.000092 | 13.458499 | 2.0 | 1.553467 | 1.0 | -248.736869 | 7.521336e-04 | 0.992672 | 0.003283 | 0.000231 | 75940.191470 | 0 | 78338.287784 | 5 | -68.488528 | 2.257037e-03 | 0.984743 | 0.000904 | 0.000092 | 75940.191470 | 0 | 78338.287784 | 5 | -68.048039 | 2.257037e-03 | 0.984743 | 0.000904 | 0.000092 | 75940.191470 | 0 | 78338.287784 | 5 | -248.736869 | 7.521336e-04 | 0.992672 | 0.003283 | 0.000231 | 75940.191470 | 0 | 78338.287784 | 5 | -386.404560 | 7.521336e-04 | 0.992672 | 0.005099 | 0.000358 | -386.404560 | 7.521336e-04 | 0.992672 | 0.005099 | 0.000358 | 75940.191470 | 77139.239627 | 78338.287784 |
| 16 | 16 | 7 | 3600.019308 | -49.432006 | 1.254883e-03 | 0.946159 | 0.000651 | 0.000100 | -49.208246 | 1.254883e-03 | 0.946159 | 0.000651 | 0.000100 | 13.193879 | 3.0 | 1.250772 | 1.0 | -226.581932 | 4.779667e-05 | 0.985555 | 0.002972 | 0.000228 | 76540.684802 | 0 | 80140.704110 | 6 | -59.321733 | 1.200094e-03 | 0.971580 | 0.000779 | 0.000095 | 76540.684802 | 0 | 80140.704110 | 6 | -59.097972 | 1.200094e-03 | 0.971580 | 0.000779 | 0.000095 | 76540.684802 | 0 | 80140.704110 | 6 | -253.568080 | 2.703497e-05 | 0.995752 | 0.003320 | 0.000154 | 76540.684802 | 0 | 80140.704110 | 6 | -317.155764 | 2.703497e-05 | 0.995752 | 0.004153 | 0.000192 | -283.402256 | 4.779667e-05 | 0.985555 | 0.003717 | 0.000286 | 76540.684802 | 78340.694456 | 80140.704110 |
| 17 | 17 | 8 | 4201.183384 | -56.353086 | 8.712980e-04 | 0.928338 | 0.000745 | 0.000122 | -56.645388 | 8.712980e-04 | 0.928338 | 0.000745 | 0.000122 | 20.648160 | 2.0 | 0.746543 | 1.0 | -515.121693 | 2.645955e-06 | 0.989783 | 0.006731 | 0.000396 | 76540.684802 | 0 | 80741.868186 | 8 | -56.353086 | 8.712980e-04 | 0.928338 | 0.000745 | 0.000122 | 76540.684802 | 0 | 80741.868186 | 8 | -56.645388 | 8.712980e-04 | 0.928338 | 0.000745 | 0.000122 | 76540.684802 | 0 | 80741.868186 | 8 | -515.121693 | 2.645955e-06 | 0.989783 | 0.006731 | 0.000396 | 76540.684802 | 0 | 80741.868186 | 8 | -384.560643 | 2.645955e-06 | 0.989783 | 0.005025 | 0.000296 | -384.560643 | 2.645955e-06 | 0.989783 | 0.005025 | 0.000296 | 76540.684802 | 78641.276494 | 80741.868186 |
| 18 | 18 | 8 | 4201.183384 | -47.728816 | 5.333120e-04 | 0.939326 | 0.000630 | 0.000094 | -46.987604 | 5.333120e-04 | 0.939326 | 0.000630 | 0.000094 | 29.877832 | 4.0 | 2.098478 | 1.0 | -284.175628 | 2.057823e-04 | 0.956015 | 0.003721 | 0.000466 | 76540.684802 | 0 | 80741.868186 | 8 | -47.728816 | 5.333120e-04 | 0.939326 | 0.000630 | 0.000094 | 76540.684802 | 0 | 80741.868186 | 8 | -46.987604 | 5.333120e-04 | 0.939326 | 0.000630 | 0.000094 | 76540.684802 | 0 | 80741.868186 | 8 | -284.175628 | 2.057823e-04 | 0.956015 | 0.003721 | 0.000466 | 76540.684802 | 0 | 80741.868186 | 8 | -596.336198 | 2.057823e-04 | 0.956015 | 0.007807 | 0.000978 | -596.336198 | 2.057823e-04 | 0.956015 | 0.007807 | 0.000978 | 76540.684802 | 78641.276494 | 80741.868186 |
| 19 | 19 | 7 | 3598.588190 | -39.294228 | 4.651180e-04 | 0.963940 | 0.000514 | 0.000063 | -38.611318 | 4.651180e-04 | 0.963940 | 0.000514 | 0.000063 | 15.595845 | 1.0 | 1.979630 | 1.0 | -136.822009 | 1.485273e-05 | 0.990960 | 0.001786 | 0.000108 | 77143.279996 | 0 | 80741.868186 | 6 | -42.144383 | 2.888146e-03 | 0.955793 | 0.000550 | 0.000085 | 77143.279996 | 0 | 80741.868186 | 6 | -41.461473 | 2.888146e-03 | 0.955793 | 0.000550 | 0.000085 | 77143.279996 | 0 | 80741.868186 | 6 | -123.330258 | 1.094731e-05 | 0.997297 | 0.001613 | 0.000059 | 77143.279996 | 0 | 80741.868186 | 6 | -244.148263 | 1.094731e-05 | 0.997297 | 0.003193 | 0.000118 | -270.856935 | 1.485273e-05 | 0.990960 | 0.003535 | 0.000214 | 77143.279996 | 78942.574091 | 80741.868186 |
Let’s find the longest track and try to visualize it:
track_table.sort_values(by=['count'], ascending=False, inplace=True)
particular_tracking_table = track_table.aux_table[0] # the first
_mapping_track_table_aux_tables = store[list(position.tables._mapping_track_table_aux_tables)[0]._v_pathname]
index = _mapping_track_table_aux_tables.query('_index == @particular_tracking_table').individual_table
the_longest_track = store[getattr(position.tables._individual_track_table_aux_tables, 'track_table_aux_tables_%09d' % (index,))._v_pathname]
the_longest_track
| distance | distance_num | node_id_a | node_id_b | node_next_id_a | node_next_id_b | timepoint | track_table_number | |
|---|---|---|---|---|---|---|---|---|
| 0 | 5.898107 | 1.0 | 0 | 1 | 0 | 1 | 42345.743439 | 0 |
| 1 | 7.083879 | 1.0 | 0 | 1 | 0 | 2 | 42943.263915 | 0 |
| 2 | 7.251955 | 1.0 | 0 | 2 | 0 | 1 | 43545.771926 | 0 |
| 3 | 8.919651 | 1.0 | 0 | 1 | 0 | 1 | 44144.751331 | 0 |
| 4 | 9.688499 | 1.0 | 0 | 1 | 0 | 1 | 44744.694663 | 0 |
| 5 | 11.311585 | 1.0 | 0 | 1 | 0 | 1 | 45344.289949 | 0 |
| 6 | 12.540052 | 1.0 | 0 | 1 | 0 | 1 | 45939.743908 | 0 |
| 7 | 14.456596 | 1.0 | 0 | 1 | 0 | 1 | 46545.171155 | 0 |
| 8 | 16.146596 | 1.0 | 0 | 1 | 0 | 1 | 47147.290182 | 0 |
| 9 | 18.101215 | 1.0 | 0 | 1 | 0 | 1 | 47744.704740 | 0 |
| 10 | 20.143139 | 1.0 | 0 | 1 | 0 | 1 | 48338.214147 | 0 |
| 11 | 22.355845 | 1.0 | 0 | 1 | 0 | 1 | 48945.238245 | 0 |
| 12 | 25.077399 | 1.0 | 0 | 1 | 0 | 1 | 49539.787734 | 0 |
| 13 | 27.538952 | 1.0 | 0 | 1 | 0 | 1 | 50142.246928 | 0 |
| 14 | 31.024734 | 1.0 | 0 | 1 | 0 | 1 | 50745.344198 | 0 |
| 15 | 33.735136 | 1.0 | 0 | 1 | 0 | 1 | 51344.796590 | 0 |
| 16 | 37.211679 | 1.0 | 0 | 1 | 0 | 1 | 51944.723954 | 0 |
| 17 | 40.819015 | 1.0 | 0 | 1 | 0 | 1 | 52542.958177 | 0 |
| 18 | 45.219417 | 1.0 | 0 | 1 | 0 | 1 | 53141.803414 | 0 |
| 19 | 49.032112 | 1.0 | 0 | 1 | 0 | 1 | 53741.353184 | 0 |
| 20 | 53.111341 | 1.0 | 0 | 1 | 0 | 1 | 54341.241176 | 0 |
| 21 | 57.906361 | 1.0 | 0 | 1 | 1 | 2 | 54942.331510 | 0 |
timepoint = the_longest_track.timepoint / (60*60)
length = the_longest_track.distance
pyplot.title('Length over Time')
pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')
pyplot.plot(timepoint, length)
[<matplotlib.lines.Line2D at 0x7f89d9621470>]
Now all tracked hyphae:
pyplot.title('Length over Time')
pyplot.xlabel('Time [h]')
pyplot.ylabel('Length [µm]')
for idx, row in track_table.iterrows():
particular_tracking_table = int(row.aux_table)
index = _mapping_track_table_aux_tables.query('_index == @particular_tracking_table').individual_table
track = store[getattr(position.tables._individual_track_table_aux_tables, 'track_table_aux_tables_%09d' % (index,))._v_pathname]
timepoint = track.timepoint / (60*60)
length = track.distance - track.distance.min()
pyplot.plot(timepoint, length)
pyplot.xlim(0, None)
(0, 22.961576228743152)
